code: def test_quantize_openvino_bf16(self):
      model = mobilenet_v3_small(num_classes=10)
  
      x = torch.rand((10, 3, 256, 256))
  
      try:
          optimized_model = InferenceOptimizer.quantize(
              model,
              accelerator="openvino",
              input_sample=x,
              precision="bf16",
          )
      except RuntimeError as e:
          assert e.__str__() == "Platform doesn't support BF16 format"
          return
  
      with InferenceOptimizer.get_context(optimized_model):
          y_hat = optimized_model(x[0:3])
          assert y_hat.shape == (3, 10)
          y_hat = optimized_model(x)
          assert y_hat.shape == (10, 10)
  
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(optimized_model, tmp_dir_name)
          new_model = InferenceOptimizer.load(tmp_dir_name)
      with InferenceOptimizer.get_context(new_model):
          y_hat = new_model(x[0:3])
          assert y_hat.shape == (3, 10)
          assert (
              new_model.ov_model.additional_config[
                  "INFERENCE_PRECISION_HINT"
              ]
              == "bf16"
          )
  
imports: from unittest import TestCase
  from torchmetrics import F1Score
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch import InferenceOptimizer
  from torchvision.models.mobilenetv3 import mobilenet_v3_small
  from torchvision.models import resnet50
  import torch
  from torch import nn
  from torch.utils.data.dataset import TensorDataset
  from torch.utils.data.dataloader import DataLoader
  import tempfile
  import pytest
  import numpy
label_desc: Use image node_id 8 for calling the TestOpenVINO obj's underlying member method code with example usage: obj.test_quantize_openvino_bf16() without return types